Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

Expert Systems with Applications

Volumn 35, Issue 3, 2008, Pages 918-928

  Niu, Gang ^a   Widodo, Achmad ^a   Son, Jong Duk ^a   Yang, Bo Suk ^a   Hwang, Don Ha ^b   Kang, Dong Sik ^b  

^a Pukyong National University   (South Korea)

^b Korea Electrotechnology Research Institute (KERI)   (South Korea)

Author keywords

Decision level fusion; Discrete wavelet transform; Fault diagnosis; Induction motor; Transient current signal

Indexed keywords

AC MOTORS; CURVE FITTING; DISCRETE WAVELET TRANSFORMS; ELECTRIC FAULT CURRENTS; EXPERIMENTS; FUSION REACTIONS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; MOTORS; NUCLEAR PHYSICS; OPTICAL DEVICES; SIGNAL PROCESSING; WAVELET DECOMPOSITION; WAVELET TRANSFORMS;

BAYESIAN; BELIEF FUSION; DECISION-LEVEL FUSION; DIAGNOSIS PERFORMANCE; DIFFERENT FREQUENCIES; DISCRETE WAVELETS; ELSEVIER (CO); EXPERIMENT RESULTS; FAULT DIAGNOSIS (FD); FUSION ALGORITHMS; MULTI AGENTS; MULTI LEVEL (ML); SIGNAL PRE-PROCESSING; START UPS; TRANSIENT CURRENT (IDD); TRANSIENT CURRENT SIGNALS; TRANSIENT STATOR CURRENT;

INDUCTION MOTORS;

EID: 44949090516     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.024     Document Type: Article

References (16)

1. Basir O., and Yuan X.H. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Information Fusion 8 4 (2007) 379-386
2. Burrus C.S., Gopinath R.A., and Guo H. Introduction to wavelets and wavelet transforms (1998), Prentice-Hall
3. Chen Y.D., Du R., and Qu L.S. Fault features of large rotating machinery and diagnosis using sensor fusion. Mechanical Systems and Signal Processing 188 2 (1995) 227-242
4. Cristianini N., and Taylor J.S. An introduction to support vector machines and other kernel-based learning methods (2000), Cambridge University Press
5. Douglas, H., & Pillay, P. (2005). The impact of wavelet selection on transient motor current signature analysis. In Proceedings of IEEE international conference on electrical machine and drives. pp. 80-85.
6. Duda R.O., Hart P.E., and Stork D.G. Pattern classification. 2nd ed. (2000), John Wiley & Sons Press
7. Hall D.L., and Llinas J. An introduction to multisensor data fusion. IEEE Digital Object Identifier 85 1 (1997) 6-23
8. Han, T. (2005). Development of a feature-based fault diagnostics system and its application to induction motors. Doctoral Dissertation. Pukyong National University.
9. Kou J.B., and Zhang C.S. Multi-agent based classifier combination. Chinese Journal of Computers (2003) 1-5 (in Chinese)
10. Niu G., Han T., Yang B.S., and Tan A.C.C. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 21 3 (2007) 1285-1299
11. Vapink V., and Chapelle O. Bounds on error expectation for SVM, advances in large margin classifiers (1999), MIT Press
12. Vapnik V. The nature of statistical learning theory (1995), Springer Verlag
13. Xu L., Krzyzak A., and Suen C. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transaction on Systems, Man, and Cybernetics 22 3 (1992) 418-435
14. Yang B.S., Han T., and An J.L. ART-Kohonen neural network for fault diagnosis of rotating machinery. Mechanical System and Signal Processing 18 3 (2004) 645-657
15. Yang B.S., Jeong S.K., Oh Y.M., and Tan A. Case-based reasoning system with Petri nets for induction motor fault diagnosis. Expert Systems with Applications 27 2 (2004) 301-311
16. Yang B.S., and Kim K.J. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing 20 2 (2006) 403-420